Solutions Architect

Solution Architect – Databricks Practice

Designation – Solution Architect – Databricks Practice

Experience – 8–14 years

Location- Pune, Bangalore, Noida, Gurugram, Hyderabad, Jaipur

Job Summary:

We are seeking a Senior Solution Architect to join our Databricks Practice and play a pivotal role in shaping data modernization journeys for enterprise customers. This is a customer-facing, consulting-oriented role where you will engage with CDOs, Data Architects, and Engineering leaders to design and deliver scalable, future-ready Lakehouse solutions on the Databricks Data Intelligence Platform.

You will be the trusted technical advisor — translating business problems into architectural blueprints, guiding customers through EDW modernization programs, and championing the adoption of Databricks most advanced capabilities including Lakeflow Declarative Pipelines (LDP), Lakeflow Connect, Serverless Compute, Unity Catalog, and AI/BI workloads.

Job Description:

1. Lead pre-sales and delivery architecture conversations with enterprise customers, owning the technical narrative from discovery through solution design and through to implementation oversight.
2. Design end-to-end Databricks Lakehouse architectures spanning ingestion, transformation, governance, ML, and consumption layers — aligned to Medallion Architecture principles.
3. Drive EDW modernization engagements, including migration strategies from legacy platforms (Teradata, Netezza, Oracle, SAS, Greenplum, Synapse, Snowflake, on-prem Hadoop) to Databricks, with clear wave planning, risk mitigation, and TCO/ROI articulation.
4. Advise customers on the adoption of advanced Databricks features, examples:
5. Lakeflow Declarative Pipelines (LDP / formerly DLT) for production-grade ETL with built-in data quality and lineage
6. Lakeflow Connect for managed ingestion from SaaS, databases, and file sources
7. Serverless Compute (SQL warehouses, jobs, notebooks) for cost and operational efficiency
8. Unity Catalog for unified governance, lineage, and fine-grained access control
9. Databricks Asset Bundles (DAB) for CI/CD and environment promotion
10. Databricks SQL, AI/BI Dashboards, Genie for self-service analytics
11. MLflow, Model Serving, and Mosaic AI for ML and GenAI workloads
12. Conduct architecture deep-dives, PoCs, and workshops — including cost modeling, performance benchmarking, DR design, and platform sizing.
13. Produce client-ready deliverables: HLD/LLD documents, reference architectures, migration roadmaps, runbooks, and executive presentations.
14. Partner with sales, delivery, and Databricks field teams to shape proposals, SoWs, and bid responses.
15. Stay ahead of the Databricks product roadmap and evangelize new capabilities internally and with customers.
16. Mentor data engineers, platform engineers, and junior architects on Databricks best practices.

Must-Have:

1. 8+ years in data engineering / data platform architecture, with 3+ years hands-on on Databricks in a customer-facing or consulting capacity.
2. Demonstrated experience leading at least 2–3 large-scale EDW modernization or Lakehouse migration programs end-to-end.
3. Strong, fundamentals-level understanding of the Databricks Data Intelligence Platform, including:
4. Delta Lake internals (transaction log, OPTIMIZE, Z-ORDER, Liquid Clustering, Deletion Vectors, Predictive Optimization)Spark execution model, Photon, cluster sizing, and performance tuning
5. Unity Catalog object model (metastore, catalogs, schemas, volumes, external locations, storage credentials)Workspace, account, and identity architecture across AWS / Azure / GCP
6. Hands-on experience designing and deploying Lakeflow Declarative Pipelines, Lakeflow Connect, and Serverless workloads in production.
7. Solid grounding in Medallion Architecture, dimensional modeling, and data governance frameworks.
8. Strong SQL and PySpark skills; comfort reading and reasoning about Spark execution plans.
9. Cloud fluency on at least one of Azure, AWS, or GCP — including networking, IAM, and storage layers (ADLS Gen2 / S3 / GCS).
10. Excellent communication and stakeholder management skills — able to engage equally with engineers and C-suite.
11. Willing to travel and stay overseas for Solutioning and Delivering projects.

Good to Have:

1. Databricks certifications: Certified Data Engineer Professional, Certified Machine Learning Professional, or Databricks Certified Solutions Architect Professional.
2. Experience with DR architectures (Delta Deep Clone, cross-region replication, RPO/RTO design).
3. Exposure to MLOps / GenAI patterns on Databricks (MLflow, Model Serving, Vector Search, Mosaic AI Agent Framework).
4. Familiarity with legacy ETL/ELT platforms (Informatica, ODI, SAS DI, DataStage, Talend) for migration credibility.
5. Experience with Terraform / Databricks Asset Bundles for IaC and CI/CD.
6. Working knowledge of Immuta, Collibra, Purview, or Alation for governance integration.
7. Industry depth in BFSI, Insurance, Retail, Manufacturing, or Healthcare.
8. What Success Looks Like (First 6–12 Months)
9. Owned the architecture for at least 2 strategic customer engagements from discovery to go-live.
10. Established yourself as the go-to advisor on advanced Databricks adoption (LDP, Lakeflow Connect, Serverless) within the practice.
11. Contributed to reusable accelerators, reference architectures, and migration playbooks that scale across the practice.
12. Built strong working relationships with Databricks field teams and contributed to joint pursuits.

Immediate Joiners Preferred

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